{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:55:09Z","timestamp":1784300109421,"version":"3.55.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031731150","type":"print"},{"value":"9783031731167","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73116-7_4","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:15:38Z","timestamp":1730301338000},"page":"56-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PDT: Uav Target Detection Dataset for\u00a0Pests and\u00a0Diseases Tree"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4911-276X","authenticated-orcid":false,"given":"Mingle","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9417-2505","authenticated-orcid":false,"given":"Rui","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7195-3413","authenticated-orcid":false,"given":"Delong","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8774-3918","authenticated-orcid":false,"given":"Zhiyong","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7896-4833","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, R., Hariharan, S., Rao, M.N., Agarwal, A.: Weed identification using k-means clustering with color spaces features in multi-spectral images taken by UAV. In: IGARSS, pp. 7047\u20137050. IEEE (2021)","DOI":"10.1109\/IGARSS47720.2021.9554097"},{"key":"4_CR2","unstructured":"Bochkovskiy, A., Wang, C., Liao, H.M.: YOLOv4: optimal speed and accuracy of object detection. CoRR arXiv:2004.10934 (2020)"},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"100187","DOI":"10.1016\/j.iot.2020.100187","volume":"18","author":"AD Boursianis","year":"2022","unstructured":"Boursianis, A.D., et al.: Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet Things 18, 100187 (2022)","journal-title":"Internet Things"},{"key":"4_CR4","unstructured":"University\u00a0of burgandy: Ribworth dataset (2022). https:\/\/universe.roboflow.com\/university-of-burgandy-zowkw\/ribworth"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Butte, S., Vakanski, A., Duellman, K., Wang, H., Mirkouei, A.: Potato crop stress identification in aerial images using deep learning-based object detection. CoRR arXiv:2106.07770 (2021)","DOI":"10.1002\/agj2.20841"},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"108467","DOI":"10.1016\/j.compag.2023.108467","volume":"216","author":"T Caras","year":"2024","unstructured":"Caras, T., et al.: Monitoring the effects of weed management strategies on tree canopy structure and growth using UAV-lidar in a young almond orchard. Comput. Electron. Agric. 216, 108467 (2024)","journal-title":"Comput. Electron. Agric."},{"key":"4_CR7","unstructured":"Carboni: weeds pytorch (2023). https:\/\/github.com\/carboni123\/weeds-pytorch"},{"issue":"10","key":"4_CR8","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1177\/0278364917720510","volume":"36","author":"N Chebrolu","year":"2017","unstructured":"Chebrolu, N., Lottes, P., Schaefer, A., Winterhalter, W., Burgard, W., Stachniss, C.: Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. Int. J. Robotics Res. 36(10), 1045\u20131052 (2017)","journal-title":"Int. J. Robotics Res."},{"issue":"9","key":"4_CR9","doi-asserted-by":"publisher","first-page":"2342","DOI":"10.3390\/rs15092342","volume":"15","author":"LS Costa","year":"2023","unstructured":"Costa, L.S., et al.: Woody plant encroachment in a seasonal tropical savanna: lessons about classifiers and accuracy from UAV images. Remote. Sens. 15(9), 2342 (2023)","journal-title":"Remote. Sens."},{"key":"4_CR10","unstructured":"Dabhi: Crop and weed detection (2021). https:\/\/github.com\/ravirajsinh45\/Crop_and_weed_detection"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"1226329","DOI":"10.3389\/fpls.2023.1226329","volume":"14","author":"Z Guo","year":"2023","unstructured":"Guo, Z., Goh, H.H., Li, X., Zhang, M., Li, Y.: WeedNet-R: a sugar beet field weed detection algorithm based on enhanced retinanet and context semantic fusion. Front. Plant Sci. 14, 1226329 (2023)","journal-title":"Front. Plant Sci."},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: more features from cheap operations. In: CVPR, pp. 1577\u20131586. Computer Vision Foundation. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"108128","DOI":"10.1016\/j.compag.2023.108128","volume":"212","author":"J Huang","year":"2023","unstructured":"Huang, J., Luo, Y., Quan, Q., Wang, B., Xue, X., Zhang, Y.: An autonomous task assignment and decision-making method for coverage path planning of multiple pesticide spraying UAVs. Comput. Electron. Agric. 212, 108128 (2023)","journal-title":"Comput. Electron. Agric."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Innani, S., Dutande, P., Baheti, B., Talbar, S.N., Baid, U.: Fuse-PN: a novel architecture for anomaly pattern segmentation in aerial agricultural images. In: CVPR Workshops, pp. 2960\u20132968. Computer Vision Foundation. IEEE (2021)","DOI":"10.1109\/CVPRW53098.2021.00331"},{"issue":"1","key":"4_CR15","first-page":"816","volume":"12","author":"B Jabir","year":"2022","unstructured":"Jabir, B., Falih, N.: Deep learning-based decision support system for weeds detection in wheat fields. Int. J. Electr. Comput. Eng. 12(1), 816 (2022)","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Jocher, G.: YOLOv5 by Ultralytics (2020). https:\/\/doi.org\/10.5281\/zenodo.3908559, https:\/\/github.com\/ultralytics\/yolov5","DOI":"10.5281\/zenodo.3908559"},{"key":"4_CR17","unstructured":"Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO (2023). https:\/\/github.com\/ultralytics\/ultralytics"},{"key":"4_CR18","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-319-67597-8_11","volume-title":"ICT Innovations 2017","author":"P Lameski","year":"2017","unstructured":"Lameski, P., Zdravevski, E., Trajkovik, V., Kulakov, A.: Weed detection dataset with RGB images taken under variable light conditions. In: Trajanov, D., Bakeva, V. (eds.) ICT Innovations 2017. CCIS, vol. 778, pp. 112\u2013119. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67597-8_11"},{"key":"4_CR19","unstructured":"Li, C., et al.: YOLOv6: a single-stage object detection framework for industrial applications. CoRR arXiv:2209.02976 (2022)"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: ICCV, pp. 16748\u201316759. IEEE (2023)","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"4_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR, pp. 936\u2013944. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2999\u20133007. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR, pp. 8759\u20138768. Computer Vision Foundation. IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"4_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"4_CR25","unstructured":"Monster: weed dataset (2019). https:\/\/gitee.com\/Monster7\/weed-datase\/tree\/master\/"},{"key":"4_CR26","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. CoRR arXiv:1804.02767 (2018)"},{"issue":"6","key":"4_CR27","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: CVPR, pp. 10778\u201310787. Computer Vision Foundation. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.compag.2017.01.001","volume":"135","author":"J Tang","year":"2017","unstructured":"Tang, J., Wang, D., Zhang, Z., He, L., Xin, J., Xu, Y.: Weed identification based on k-means feature learning combined with convolutional neural network. Comput. Electron. Agric. 135, 63\u201370 (2017)","journal-title":"Comput. Electron. Agric."},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Wang, C., Bochkovskiy, A., Liao, H.M.: YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: CVPR, pp. 7464\u20137475. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.00721"},{"issue":"1","key":"4_CR31","first-page":"23","volume":"3","author":"R Zhang","year":"2020","unstructured":"Zhang, R., Wang, C., Hu, X., Liu, Y., Chen, S.: Weed location and recognition based on UAV imaging and deep learning. Int. J. Precis. Agric. Aviat. 3(1), 23\u201329 (2020)","journal-title":"Int. J. Precis. Agric. Aviat."},{"key":"4_CR32","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. CoRR arXiv:1904.07850 (2019)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73116-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:18:12Z","timestamp":1730301492000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73116-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031731150","9783031731167"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73116-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}